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The Impact of Artificial Intelligence on Business

Snowflake Snowday 2021

As it transforms traditional methods of working, the impact of artificial intelligence (AI) on business is being felt deeply across many industries. AI has the ability to apply human-like reasoning and decision-making at scale, empowering businesses in a variety of ways. In this article, we’ll explore why AI has become such an integral part of many organizations’ long-term strategies and share examples of how modern businesses are applying AI. To wrap up, we'll share the unique role Snowflake plays in fueling these advances. 

Why use artificial intelligence for business?

In today’s data-centric business landscape, the ability to maximize the use of data is crucial. This has spurred many organizations to invest heavily in AI-enabled technology. Here are three primary reasons why AI’s impact on business has been so significant.

Increased efficiency

One of AI’s greatest impacts on business is its ability to save time and resources. AI can be used to automate repetitive and time-consuming tasks such as responding to customer service requests, assessing the creditworthiness of borrowers and processing insurance claims. This automation provides faster, more responsive service for customers, and it frees employees to focus on more strategic, higher-level work while eliminating the risk of human error. 

Supported decision-making

AI is able to sift through enormous amounts of data quickly, often in real-time, to uncover valuable insights that might be difficult or impossible for humans to identify. Demand forecasting is a prime example of this technology’s growing role in supported decision-making. By drawing together and analyzing data from internal and third-party data sources, AI can help decision-makers in a variety of tasks, such as predicting their costs, identifying and reacting to potential supply chain disruptions and more accurately predicting product demand. AI-supported data analysis creates new opportunities for improved strategic planning, better resource allocation and more proactive risk management.

Innovation and new opportunities

By identifying new patterns and combinations, AI sparks innovation and discovery. It can be used to develop and test prototypes virtually, eliminating the traditional trial-and-error approach to new product development. It can also be used in competitive intelligence, gathering and analyzing publicly available data on competitors to develop an in-depth understanding of their products and pricing strategies.

5 examples of artificial intelligence in business

Artificial intelligence in business has been broadly adopted, with AI-powered technologies now an integral part of daily operations in many businesses. Let’s look at five specific use cases that illustrate how AI is creating new opportunities, sparking innovation and helping organizations evolve to thrive in highly competitive industries.

Healthcare

AI-supported digital pathology image analysis is helping physicians identify difficult-to-detect diseases such as cancer in its early stages, enabling patients to begin treatment sooner. Artificial intelligence is also being used to accelerate drug screening and molecule design, facilitating more rapid development of new medications.

Transportation and logistics

With so much dynamic data such as vehicle location, speed, geo location, maintenance history, mileage, engine performance and cargo details, it is difficult for fleet managers to run an efficient and cost-effective fulfillment process without the use of AI. They can more efficiently maintain operations through AI-enabled predictive maintenance and reduce driver turnover by securely analyzing HR data. With vital information at their fingertips, managers can reroute shipments based on the latest traffic conditions, taking into account delivery schedules and SLAs. 

Manufacturing 

Capturing data from manufacturing equipment and other shop floor systems, AI can better predict the need for maintenance activities. This reduces unplanned downtime and prolongs the life of equipment. AI also plays an important role in quality control by identifying root causes and alerting for anomalies, reducing scrap and rework costs. AI-enabled image recognition can identify product defects, allowing quality control managers to take corrective action before poor-quality products are shipped to customers.

Finance

AI is a powerful tool for fraud prevention. It can detect unusual patterns and anomalies in financial transactions, such as credit card usage, allowing financial institutions to identify and block potentially fraudulent activity. Its ability to identify complex connections within that data makes it an essential ingredient in modern risk management activities, helping financial institutions better understand and manage risks associated with investments, loans and insurance underwriting decisions.

Energy 

Electric utilities can leverage AI for planning and operation of distribution and transmission grids, including optimizing maintenance to improve grid reliability. This technology can also be used to maximize the production and revenue of renewable energy sources such as wind and solar by forecasting renewable energy production and market prices and optimizing production schedules. 

The role of the Snowflake Data Cloud in AI business strategy

AI has given rise to a new frontier—one in which advanced analytics and supported decision-making inform business strategy. Generative AI (GenAI) and large language models (LLMs) are changing how we work at a global scale. Snowflake has brought the Data Cloud’s security, ease of use and governance to GenAI and ML to help organizations across many industries realize the full potential of AI for business and simplify the complexities of managing, analyzing and extracting actionable insights from large, diverse data sets. Here are four unique features this dynamic and scalable ecosystem offers for powering today’s AI-driven business initiatives.

Accelerate feature engineering and simplify AI/ML training

Snowpark ML APIs enable easier end-to-end ML development and deployment of artificial intelligence tools in Snowflake. Snowpark ML APIs currently include the ML Modeling API, now in public preview, and the ML Operations API, currently in private preview. 

On the development side, the Snowpark ML Modeling API scales out feature engineering, simplifies model training in Snowflake and allows for the implementation of sklearn-style processing natively on data in Snowflake without the need for creating stored procedures but while taking advantage of parallelization. It also provides a simpler, more streamlined user experience, allowing data scientists to train models with familiar APIs directly on data in Snowpark by using sklearn and XGBoost natively on data without importing through stored procedures.

Store and govern all of an organization’s AI/ML models

Once a model has been developed, data scientists can seamlessly deploy their model in Snowflake with the Snowpark ML Operations API, which includes the Snowpark Model Registry, currently in private preview. This feature provides a unified repository for an organization’s ML models, helping them to streamline and scale their machine learning model operations (MLOps). The registry centralizes the publishing and discovery of models, simplifying the process data scientists use to hand off their successful experiments to ML engineers for deployment as models in production on Snowflake infrastructure. 

Visualize data and ML models with interactive web apps

Streamlit in Snowflake, coming to public preview soon, brings data and ML models to life with interactive apps built using Python. This feature will bring together Streamlit’s user-friendly data app development library with the scalability, reliability, security, and governance of the Snowflake platform, giving data scientists and Python developers the ability to rapidly transform their data and models into interactive, enterprise-ready applications.

Simplified streaming data pipelines (Dynamic Tables and Snowpipe Streaming)

Snowflake is expanding streaming capabilities with Dynamic Tables and Snowpipe Streaming. Dynamic Tables, now in public preview, simplify the creation of continuous data pipelines used for transforming both batch and streaming data, an essential ingredient for training AI models. With Dynamic Tables, you can declaratively build continuous data pipelines and process incremental refreshes cost effectively. Together with Snowpipe Streaming, generally available, Snowflake removes the boundaries between batch and streaming systems, making it easier than ever before to use streaming pipelines to extract value from time-sensitive data. 

Fuel your AI-enabled business strategy with Snowflake 

AI plays a foundational role in the development and execution of a modern data-driven business strategy. Snowflake delivers AI-driven functionality, including built-in functions and UIs, helping organizations accelerate their workflows with fast data access and elastically scalable data processing for Python and SQL. With one place to instantly access all relevant data, businesses are free to focus their full attention on extracting and using the actionable insights from their data.